Relvy AI
Autonomous AI on-call engineer that investigates alerts and produces auditable investigation notebooks.
Relvy is a focused buy for platform and SRE teams that already run mature observability and want a consistent, auditable investigation process rather than ad-hoc debugging. Its differentiation is the autonomous agent plus the notebook artifact: it queries telemetry and code, runs on a context layer of runbooks and prior incident memory, and produces a reviewable record your team can export as a post-mortem. That audit trail is the real value, and it is what generic AI copilots such as a raw ChatGPT or Claude chat session do not give you. The trade-offs are structural: Relvy's output is only as good as your telemetry and runbooks, and you need to run the agent inside your environment. We
Verified 12d ago · liveness 59/100 · cite: rightaichoice.com/tools/relvy-ai
- SRE and platform teams with high on-call alert volume
- Organizations that already maintain runbooks and want them executed automatically
- Teams standardizing incident response across multiple services
- Companies needing auditable investigation trails for compliance
- Teams without a mature observability stack, since the agent's context depends on it
- Engineers who want a general-purpose AI coding assistant or chatbot
- Teams whose debugging is entirely exploratory and has no repeatable steps to automate
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Skip Relvy if your debugging is genuinely exploratory or if your observability stack is too thin to give an agent runbooks, telemetry, and traces to investigate.
Relvy positions forward deployed engineers to tune the agent for your stack, which typically implies an onboarding or services commitment on top of the software itself.
We could not reach Relvy's pricing page, so we cannot place it on a price spectrum against cheaper incident-triage tools or against enterprise observability suites that bundle AI investigation features. Budget it as an enterprise software line item alongside your Datadog, New Relic, or Splunk spend rather than as a per-seat productivity tool, and expect the commercial conversation to include a tuning engagement.
In short
Relvy AI — Autonomous AI on-call engineer that investigates alerts and produces auditable investigation notebooks. Best for SRE and platform teams with high on-call alert volume, Organizations that already maintain runbooks and want them executed automatically, Teams standardizing incident response across multiple services. Contact Sales pricing.
What's new in Relvy AI
Checked 5 days agoAcross the latest 2 updates: 1 launch and 1 news mention.
Relvy improved Claude's RCA accuracy by 12pp on OpenRCA
Relvy published a homepage announcement that it improved Claude's root-cause-analysis accuracy by 12 percentage points on the OpenRCA benchmark, claiming a measurable gain in how well the agent diagnoses incident causes.
Tuneloop by Relvy AI — the outcomes layer for your AI agents
Relvy marked Tuneloop as coming soon on its homepage, describing it as an outcomes layer for AI agents. No availability date or capability detail was published at the time of scraping.
What people actually say about Relvy AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
4 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Promises to automate repetitive runbook steps for on-call engineers.
- +Integrates with existing observability and incident management tools.
- +Structured investigation templates could standardize incident response.
- +AI copilot may reduce mean time to diagnosis (MTTD).
- +Post-incident exportable reports help with blameless post-mortems.
- −Zero independent user reviews or testimonials available publicly.
- −No evidence that AI suggestions are accurate or trustworthy.
- −Limited integration list; may not cover all monitoring tools teams use.
- −No free tier or trial to test before committing to sales process.
- −Unknown pricing may exclude small or mid-size engineering teams.
- • Unknown usage-based fees for AI queries or telemetry ingestion
Viability Score
How well maintained and how widely used is Relvy AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Autonomous alert investigation executing multi-step debugging procedures
- Interactive investigation notebooks with rich visualizations
- Shared debugging sessions teammates can join and comment on in real time
- Log analysis across multiple services and hostnames
- Metrics and dashboard querying against time-series data
- APM and distributed trace analysis
- Deployment and event correlation
- Code repository analysis
- Internal API calls via MCP tools
- Plain-text runbook import and execution
- AI-assisted runbook creation
- Continuously updated context layer with runbooks and prior incident memory
- Structured post-mortem export from a completed investigation
- REST API for automating investigation workflows
- Self-host deployment option
About Relvy AI
Relvy AI is an autonomous agent built specifically for incident response. When an alert fires from PagerDuty or another monitoring tool, Relvy runs the investigation steps a senior engineer would: it queries logs, metrics, APM traces, deployments and events, inspects code repositories, and can call your internal APIs through MCP tools. Rather than starting from scratch, it runs on a continuously updated context layer of your runbooks, prior investigation memory, and live integrations, so each investigation starts with priors about what is likely and what to check. The output is an interactive investigation notebook with rich visualizations that teammates can join, comment on, and export as a structured post-mortem. It is designed for SREs, on-call engineers, and platform teams at organizations with a mature observability stack (Datadog, New Relic, Grafana, Splunk, AWS CloudWatch) and an existing incident management workflow. Relvy is backed by Y Combinator and states SOC 2 Type II compliance with self-host deployment options. It claims 70% of alerts are resolved in under five minutes. Unlike generic AI copilots where you paste an error log into a chat window, Relvy operates inside your stack and leaves an audit trail. It is not a general-purpose coding assistant or a chatbot.
Behind the Verdict
Relvy's core claim is that it turns an alert into a completed, reviewable investigation instead of a page and a scramble. Read the homepage carefully and the mechanism is concrete: an autonomous agent with purpose-built tools for logs, time-series metrics, dashboards, APM traces, deployment events, code repositories, and internal APIs via MCP tools. The agent executes plain-text runbooks, which Relvy says you can import from existing docs or generate with AI assistance. That matters because it removes the usual blocker for incident automation: not every team has machine-readable runbooks, and Relvy lets you start from the prose docs you already have. The second half of the product is the notebook. Each investigation becomes an interactive session with visualizations that teammates can open, follow, and comment on, then export as a structured post-mortem. If your organization has ever had an incident where the post-mortem was reconstructed days later from Slack scrollback, that artifact is the pitch. Where Relvy fits best: teams with real telemetry volume (millions of log lines, time-series data) and a repeatable on-call rotation. The vendor's own framing, forward deployed engineers tuning Relvy for your stack plus SOC 2 Type II and self-host options, points at mid-market and enterprise rather than a two-person startup. Where it doesn't fit: a team without a mature observability stack, or one whose debugging is genuinely exploratory rather than repeatable, will not get the value, because the agent's accuracy depends on the context it can pull. It is also not a general-purpose AI assistant; you would not use it to write application code or answer product questions. The honest caveats. The 70% under five minutes figure is the vendor's own claim and we have no independent confirmation; treat it as directional. Integration depth per tool varies, and the vendor's homepage does not enumerate per-integration capabilities. Finally, we were unable to reach Relvy's pricing page, so we cannot tell you what it costs, whether there is a free tier, or how contracts are structured. That is a gap in our knowledge, not a statement about the vendor.
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Real-world workflow fit
Concrete scenarios for the personas Relvy AI actually fits — and what changes day-one when you adopt it.
PagerDuty fires a high-severity alert for P99 latency above 2 seconds on checkout-service for five minutes. Relvy picks it up, runs checkout-service-runbook.md, and identifies the endpoints with the highest correlated latency by querying New Relic.
Outcome: The engineer opens a notebook where the dead-ends are already eliminated, confirms the finding, and moves to remediation instead of spending the first fifteen minutes gathering context.
The team imports its existing plain-text runbooks into Relvy and lets the agent execute them across the observability stack as alerts arrive, rather than relying on each engineer knowing the procedure by memory.
Outcome: Investigation steps become consistent across the whole rotation, new hires inherit institutional knowledge on day one, and the team gets fewer context switches per incident as the org scales.
During a live incident, teammates join the shared investigation session and comment on the agent's reasoning. After resolution, the notebook is exported as a structured post-mortem.
Outcome: The post-mortem is written from the actual investigation steps with visualizations attached, so the review is a discussion of what happened rather than a reconstruction from memory.
Use Cases
- An alert fires at 10:42 AM and Relvy runs the service's runbook, querying New Relic and pulling the relevant log lines before a human opens a laptop
- Debug a production incident by pulling logs, metrics, and traces into a single notebook
- Trace whether upload failures in a document service correlate with 500 errors on a specific hostname
- Plot auth-failure counts over two days to see whether a spike aligns with a deployment event
- Run a cleanup job's last three days of logs to check whether a specific client saw failures
- Join a live shared debugging session and annotate the agent's reasoning while it works
- Export a finished investigation as a structured post-mortem document
- Standardize incident response across teams by importing existing runbooks and letting the agent execute them
Models Under the Hood
as of 2026-09-09
Limitations
- Relvy's effectiveness depends on the quality and volume of your telemetry and runbooks; a thin observability setup gives the agent less to work with and less to be right about.
- The 70%-under-five-minutes figure is the vendor's own claim and is not independently verified here.
- Integration depth varies per tool, and Relvy's public pages do not enumerate per-integration capability.
- It is purpose-built for incident investigation, so it will not serve as a general-purpose AI assistant, code generator, or chat interface.
as of 2026-09-26
Verification history
We have re-verified Relvy AI 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Relvy AI's pricing actually pencils out — and where peers do it cheaper.
We could not reach Relvy's pricing page, so we cannot place it on a price spectrum against cheaper incident-triage tools or against enterprise observability suites that bundle AI investigation features. Budget it as an enterprise software line item alongside your Datadog, New Relic, or Splunk spend rather than as a per-seat productivity tool, and expect the commercial conversation to include a tuning engagement.
Setup time & first value
How long it actually takes to get something useful out of Relvy AI — broken out by persona, not the marketing-page minute.
Relvy deploys into your environment: the homepage shows a git clone, cd, and ./install.sh start quick start. Realistically budget days rather than minutes before first value, because the agent needs your integrations wired up and your runbooks imported before it can investigate anything meaningfully. Expect the forward-deployed-engineer tuning step to extend that for larger stacks.
Switching to or from Relvy AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual on-call debugging: import your existing plain-text runbooks so the agent executes the same steps your engineers already follow
- →From a generic AI copilot: move from pasting error logs into a chat window to having the agent query your telemetry directly inside your stack
- →From unreviewed incident automation: adopt investigation notebooks so each automated action leaves an auditable record
- ↗To a general-purpose AI assistant: if your need shifts to code writing or product Q&A, a coding copilot is the better fit
- ↗To a bundled observability suite: if your monitoring vendor ships native AI investigation at no extra line item, the cost math can favor staying put
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Relvy AI”, and we withheld 5: 5 could not be judged, because “Relvy AI” is a single word that other videos use for other things. Showing the 1 we can prove is about Relvy AI.
Official links
Tools that pair well with Relvy AI
Common stack mates teams adopt alongside Relvy AI, with the specific reason each pairing earns its keep.
Sazabi
Sazabi is AI-native observability: it replaces dashboards with chat debugging, autonomous alerts, and coding agents that open fix PRs.
Deeptrace
AI SRE agent that investigates production alerts and posts evidence-backed root causes in Slack within minutes.
Corelayer
AI SRE for production incident response that root-causes alerts and opens fix PRs in your own cloud
Featured Head-to-Head Comparisons
Relvy Ai vs Spider Cloud
Choose Spider Cloud if you need a high-speed, cost-effective API for crawling the web and feeding data into AI agents or RAG pipelines — its freemium model, 99.9% success rate, and new Browser AI commands make it a strong choice for developers. Choose Relvy AI if your team’s pain point is production incident response: its notebook-based debugging with AI copilot and seamless observability integrations are purpose-built for on-call engineers. They solve entirely different problems, so your decision hinges on whether you need external data extraction or internal system debugging tools.
Relvy Ai vs Temporal Ai
If you need to build fault-tolerant AI agents or orchestrate multi-step microservices that survive crashes, Temporal AI is the clear choice with its open-source durability, rich SDKs, and recent serverless workers. But if your pain point is debugging production incidents faster, Relvy AI offers a more focused, AI-powered notebook environment for on-call engineers. Choose based on your primary workflow: reliable execution vs. incident analysis.
Relvy Ai vs Voyage Ai
Voyage AI and Relvy AI serve completely different use cases: Voyage is for teams building high-accuracy RAG systems needing domain-specific embeddings and long-context support, while Relvy is for incident responders needing AI-assisted debugging notebooks with observability integrations. Choose based on your primary workflow — neither is a direct substitute.
Marvin vs Relvy Ai
Choose Marvin if you're a Python developer needing to add LLM smarts to your code with minimal fuss—it's free, open-source, and gets you from zero to AI-powered function in minutes. Choose Relvy AI if you're an SRE drowning in alerts and need an autonomous agent that investigates incidents using your existing observability stack, producing auditable notebooks. The tools solve completely different problems, so your choice hinges on whether you're building AI features or automating on-call response.
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